Prediction and validation of mild cognitive impairment in occupational dust exposure population based on machine learning.

Journal: Ecotoxicology and environmental safety
PMID: 39332198

Abstract

OBJECTIVE: Workers exposed to dust for extended periods may experience varying degrees of cognitive impairment. However, limited research exists on the associated risk factors. This study aims to identify key variables using machine learning algorithms (ML) and develop a model to predict the occurrence of mild cognitive impairment in miners.

Authors

  • Fulin Cai
    School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85287, USA.
  • Sheng Xue
    Anhui University of Science and Technology, Huainan, China. Electronic address: sheng.xue@aust.edu.cn.
  • Guangyao Si
    University of New South Wales, Sydney, Australia. Electronic address: g.si@unsw.edu.au.
  • Yafeng Liu
    Information Engineering University, Lanzhou 730050, China.
  • Xiufeng Chen
    Department of General Surgery, Beijing Aerospace General Hospital, Beijing, China.
  • Jiale He
    School of Public Health, Wenzhou Medical University, Wenzhou, China.
  • Mei Zhang
    Clinical and Research Center for Infectious Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, China.

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